What this broad demographic representation means is that Twitter may now be set to become the new holy grail for researchers looking for insights into what a wide range consumers are thinking and talking about.

But rather than using Google Blog Search, I’d suggest using the new RSS feed functionality that Google Alerts rolled out a couple of months ago, in order to aggregate the significant amount of online content that exists outside of formal blogs.

1.) Develop Key Words To Track

This could be a brand name (“Energizer”) or a specific topic “homemade barbecue sauces”. Start by typing the word or phrase in quotes in Google to see how relevant results are with the phrase.

If your brand name is also a word with multiples meanings such as “Tide”, you may need to add something like the word detergent to keep from capturing conversations on surfing or beach combing.

You can also track:

A URL for a Website

A person’s name or online nickname

2.) Getting the search feeds:

Once you have the keywords, it’s simply a matter of setting up a search feed with different social media monitoring tools. For the three listed below, that means adding your keywords to their search box and then clicking on the RSS subscription button to get the auto-link:

His topic on levering search data for consumer insights is one of my key interest areas on the bleeding edge of marketing research, and he did a great job of demonstrating the predictive power of modeling search data trends.

Google has gone here before as well, with their demonstration of the Google Flu Trends application, and how search data can be predictive of CDC confirmation of regional flu outbreaks by a couple of weeks.

Both of these examples illustrate how the modeling of aggregated search queries can be an incredible source of insights into consumer intent.

There are a couple of white space areas for marketing research with search data, and all worthy of further pursuit. For me these include:

Analyzing search terms associated with digital marketing campaigns at the metro area level in order to link digital behavior to a store level or DMA based marketing mix model.

Identifying the most predictive search terms (“grocery coupons”) that best correlate with widely tracked consumer attitude and behavior metrics (Conference Board’s Consumer Confidence Index) in order to understand where consumer sentiment is heading before the competition does.

Google Flu Trends is based upon the aggregation and analysis of the search behavior of people who type the flu symptoms they are experiencing into Google in order to confirm their self diagnosis and to look up potential treatment options.

Google has found there is a close relationship between the amount of people searching on flu symptom related keywords and the amount who actually have the flu itself.

In the chart above you can see how Google Flu Trends has been well correlated with data from the Center of Disease Control on the level of flu cases being reported in the US over the past several years.

The advantage with Google Flu Trends is that the data is available a couple of weeks ahead of what the CDC compiles and announces.

With the 2008 Presidential election now almost a week behind us, the media is filled with backwards looking punditry on what lessons this campaign will inform history with.

But of all the unique aspects of this campaign, one thing that stood out was the use of data and how that influenced strategy, especially with the Obama campaign.

His unique electoral strategy of looking outside the typical swing states into areas where Republicans have always been strong (Colorado, North Carolina, Virginia) was driven by statistical analyses that showed how changing demographics in these typically Republican areas provided opportunities for a Democrat willing to take advantage of them.

I tend not to be very politically minded, but one site that fascinated me throughout the election season was Nate Silver’s FiveThirtyEight.com blog (538 being the number of electors in the Electoral College).

Throughout the campaign, he aggregated all the available polls and then analyzed them using regression analyses to find out what their outlier tendencies tended to be.

He then weighted the polls and re-simulated the election 10,000 times per update in order to, in his words, “provide a probabilistic assessment of electoral outcomes based on a historical analysis of polling data since 1952″.

Additionally, when the returns came in on election night, it was found that “Mr. Silver had predicted the popular vote within one percentage point, predicted 49 of 50 states’ results correctly, and predicted all of the resolved Senate races correctly”.

What will be interesting to see is how this new approach to the analysis of polling data will have an effect on future elections. What is certain is that the data driven approach to election strategy is probably here to stay.

Is RSS, the backbone of content distribution online, tapped out from broader consumer standpoint?

According to Forrester Research it is:

With only 11% of consumers using RSS, and of the remaining 89% of those who don’t use RSS feeds, only 17% of them saying they are interested in using them in the future, it looks like broadly distributed online content has a dark future.

I agree that RSS is a geeky term and most of the broader base of the US public still don’t use RSS readers.

But just like Apple was able to use consumer insights to make music downloads and the mobile internet interesting to the broader public, and Google was able to make search engines decidedly not techie, I think there is a great opportunity for someone to take all the content currently available in RSS format and make it as easy to access as the evening news on TV.

RSS in its current form may not be the answer, but that doesn’t mean we should be reading its obituary.

Google Insights allows users to analyze and compare different search terms by showing patterns in search volume over time, group top related search terms, and show which of those related terms are rising or falling in popularity. It also allows you to slice and dice the data by different date ranges and geographical locations.

Below is a quick chart I did analyzing the search interest of a couple top social networking sites by comparing the search volumes associated with their names over the past year and a half (click for larger image).

Google Insights for Search

In a very intuitive and and clean manner, the tool shows how MySpace’s search popularity has plateaued, while Facebook has rapidly overtaken it in the past several months. It also shows the rapid rise of interest in the fast growing site, Hi5.

This tool is very granular, allowing users to drill down to very specific localities (e.g., Madison, WI) or time frames (e.g., last week). Which means it has strong utility for local and seasonal search analyses.

You can even filter results by category, so you can analyze results for “apple” the fruit, rather than the company Apple, which dominates the search traffic for that word.

Marketers in particular can use Google Insights to analyze the popularity over time of different trends, topics, products, or even marketing campaigns.

Google Insights is clearly a tool that can mine Google’s massive “database of intentions” for a vast range of different insights and applications.

“Tradigital, in my opinion, means using traditional marketing methods in the digital space. For example, creating an advertising campaign and “extending it digitally” usually ends up as a checklist. Micro-site? Check. Online banners? Check. Social media? Check. Mobile? Check.”

His answer to better digital marketing is a staple of what good marketers have done well in the past, which is understanding consumer behavior, this time in the digital space:

“It’s time to come to terms with how people really use the web (hint — it might not be to figure out your experimental navigation) and how we can harness the true power of digital.”

The way most people use the web, in contrast to something like watching TV, is as an active medium, rather than passive.

Whether it is asking questions through search, uploading family photos to Flickr, or communicating with friends through social networking, most of the time spent on the web is spent doing something. Or, as Armano puts it, solving problems.

Which is why traditional interruption marketing like flashing banner ads, are not only ineffective, but in most cases, very irritating in their distraction.

A solution to better digital marketing would be to look at the top reasons why people use the internet and then ask how your digital marketing efforts can enhance their activities rather than distract:

How can your digital marketing help people better connect with their friends or people with similar interests?

How can your digital marketing connect your core consumers with the music or video content they really want to see?

Are your digital marketing efforts genuinely entertaining and are they something people would want to share with their family and friends?

How is your digital marketing helping people search for information quicker or more reliably?

I know it seems odd to ask digital marketing to simply lever what Facebook or YouTube are doing already. It doesn’t seem groundbreaking or that creative.

But therein lies the point: how effective do you think your flashing banner ad is when it only serves to stand in the way of what people really want to do online?

Versus visiting a mall or any other type of shopping center, many online retailers tends to be singularly focused on one aspect of shopping, the final purchase.

There is no sense of wandering through the aisles, watching others as they shop, or having someone with you to provide advice.

What is missing is the social aspect of buying something, which is one of the foundations of the Web 2.0 experience. And for some online retailers who are not optimized to take advantage of this, this is a missed opportunity.

The best part of this example, as demonstrated in this video case study done by Shel Israel at FastCompany.tv, is how they were able to provide concrete measurement of results from the campaign through multiple sources, including use of custom surveys and online site statistics.

Through their research, they were able to clearly separate those visitors who came through their Social Media efforts, versus the rest of the people who visited the park on a daily basis.

As Carlos Garcia, CEO and co-founder of Scrapblog says of the initiative:

“A Carnival cruiser comes back and has pics and video that are essentially already branded. When they share it with friends and family, they’re sharing the brand. Allowing them to create a scrapbook online increases the number of people that can interact with the brand exponentially.”

Additionally, like the Sea World Example, Carnival also was able to get a better understanding of their initiative through concrete performance metrics by tracking the number of scrapblogs created by their guests, visit stats to the created scrapblogs, and registered conversions at CarnivalConnections.com due to scrapblog visits.

Creating effective Social Media Marketing campaigns is good first step for brands. Measuring that effectiveness on the back end is the critical next step that all brands should be taking as well.